{"_buckets": {"deposit": "90838204-1f8d-4c3a-92c7-8229f12f2803"}, "_deposit": {"id": "3881", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "3881"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00003881"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "1997-05", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "183", "bibliographicPageStart": "75", "bibliographicVolumeNumber": "21", "bibliographic_titles": [{"bibliographic_title": "Computerized Medical Imaging and Graphics"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "We have investigated a neural network classifier based on CT findings extracted by a radiologist for the differential diagnosis between the pancreatic ductal adenocarcinoma and mass-forming pancreatitis, and compared its classification performance with that of Bayesian analysis, Hayashi\u0027s quantification method II, and radiologists. The three computerized classification methods were designed to classify categorized CT findings extracted by a radiologist, and were trained and tested on 71 cases. There was comparable performance of the neural network, the Bayesian analysis, Hayashi\u0027s quantification method II, and the radiologists, in classifying pancreatic carcinoma and inflammatory mass.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/5311"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Elsevier"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1016/S0895-6111(97)00006-2", "subitem_relation_type_select": "DOI"}}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "07304862", "subitem_source_identifier_type": "ISSN"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u6c60\u7530, \u5145"}], "nameIdentifiers": [{"nameIdentifier": "9796", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ikeda, Mitsuru"}], "nameIdentifiers": [{"nameIdentifier": "9797", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u4f0a\u85e4, \u8302\u6a39"}], "nameIdentifiers": [{"nameIdentifier": "9798", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ito, Shigeki"}], "nameIdentifiers": [{"nameIdentifier": "9799", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u77f3\u57a3, \u6b66\u7537"}], "nameIdentifiers": [{"nameIdentifier": "9800", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ishigaki, Takeo"}], "nameIdentifiers": [{"nameIdentifier": "9801", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yamauchi, Kazunobu"}], "nameIdentifiers": [{"nameIdentifier": "9802", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u5c71\u5185, \u4e00\u4fe1"}], "nameIdentifiers": [{"nameIdentifier": "9803", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "Panc_Paper_Comp_m_i_g_bind.pdf", "filesize": [{"value": "366.2 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 366200.0, "url": {"label": "Panc_Paper_Comp_m_i_g_bind.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/3881/files/Panc_Paper_Comp_m_i_g_bind.pdf"}, "version_id": "905fc3dd-41c3-40bd-a4e2-889151ee7b6c"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "Computer aided diagnosis", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Neural network", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Receiver operating characteristic curve (ROC)", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Radiology and radiologists", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Pancreas", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Evaluation of a neural network classifier for pancreatic masses based on CT findings", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Evaluation of a neural network classifier for pancreatic masses based on CT findings"}]}, "item_type_id": "10", "owner": "1", "path": ["499/500/501"], "permalink_uri": "http://hdl.handle.net/2237/5311", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2006-01-20"}, "publish_date": "2006-01-20", "publish_status": "0", "recid": "3881", "relation": {}, "relation_version_is_last": true, "title": ["Evaluation of a neural network classifier for pancreatic masses based on CT findings"], "weko_shared_id": 3}